Spatial Attention Appears Modulated by Behaviourally Relevant Contexts
Bibliographic record
Abstract
It is well-documented that visual spatial attention can be modulated by the visual features of objects in the environment if the features contain semantic information, especially when behaviourally relevant (e.g., emotional facial expressions). The current study demonstrated that observers could prioritize attention toward specific object features when, and only when, the object becomes relevant within a certain behaviourally relevant context. In the current study, using virtual 3-D technology, we presented to licensed drivers a modified cue-target paradigm where a peripheral cylinder cue was followed by a peripheral roadside pedestrian target. Participants discriminated the hand/arm position of the pedestrian with a button-press on a steering wheel. The pedestrian target could appear on the same or different side of the road as the cue. In addition, pedestrians could appear oriented toward the road or away from the road—but this feature remained irrelevant to the participants’ responses. Through three experiments, we consistently found that, in the 3-D experimental condition where participants ‘drive’ within a virtual simulation, the cueing effect was significantly larger when pedestrians were facing towards the road compared to away from the road. This revealed enhanced attention towards targets—specifically those facing the road—in the cued location while driving. In contrast, this sensitivity for pedestrian orientation was not present in the three control conditions: 1) 3-D Stationary (non-driving), 2) 2-D Stationary (non-driving), and 3) another 3-D Driving scenario with an inanimate light-post target. These results suggest that drivers have heightened attention to pedestrians facing the road even though the pedestrian orientation was task-irrelevant. Licensed drivers likely demonstrated a preparatory mechanism to prioritize attention toward an event that may indicate a probability of impending collision. This novel phenomenon may be unique only to over-learned tasks such as driving (even simulated). These findings present additional evidence in favour of an embodied account of attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".